A new isomers of thiourea derivatives, namely N-(4-chlorobutanoyl)-N'-(2-methylphenyl)-thiourea (1a), N-(4-chlorobutanoyl)-N'-(3-methylphenyl)thiourea (1b) and N-(4-chlorobutanoyl)-N'-(4-methylphenyl)thiourea (1c) have been synthesized by refluxing mixture of equimolar amounts of 4-chlorobutanoylisothiocyanate with 2, 3 or 4-toluidine, respectively. The three isomers were characterized by spectroscopic (UV/vis, FT-IR and NMR) and X-ray crystallography techniques. To investigate the isomerization effect on spectroscopic data, DFT and TD-DFT calculations have been carried out using five hybrid functionals (B3LYP, B3P86, CAM-B3LYP, M06-2X and PBE0) to predict UV/vis absorption bands (n→π∗ and π→π∗), (1)H and (13)C NMR chemical shifts, FT-IR vibration modes and X-ray parameters (bonds, bond angles and torsion angles) for 1a, 1b and 1c isomers. The results showed that the isomerization effect is significant on λ(MAX) absorption bands, while for IR and NMR the effect is negligible. In accordance with previous studies, B3LYP, B3P86 and PBE0 gave the most reliable to predict the excitation energies of thiourea derivatives.
Novel series of 2-(4,6-dimethoxy,1,3,5-triazin-2-yl) amino acid ester derivatives were synthesized using simple one pot method in methanol. The products were obtained in high yields and purities as observed from their spectral data, elemental analyses, GC-MS and X-ray crystallographic analysis. The B3LYP/6-311G(d,p) calculated molecular structures are well correlated with the geometrical parameters obtained from the X-ray analyses. The spectroscopic properties such as IR vibrational modes, NMR chemical shifts and UV-Vis electronic transitions were discussed both experimentally and theoretically. The IR vibrational frequencies showed good correlations with the experimental data (R(2)=0.9961-0.9995). The electronic spectra were assigned based on the TD-DFT results. Intense electronic transition band is calculated at 198.1nm (f=0.1389), 204.2nm (f=0.2053), 205.0 (f=0.1704) and 205.7 (0.2971) for compounds 6a-i, respectively. The molecular orbital energy levels contributed in the longest wavelength transition band were explained. For all compounds, the experimental wavelengths showed red shifts compared to the calculations due to the solvent effect. The NMR chemical shifts were calculated using GIAO method. The NBO analyses were performed to predict the stabilization energies due to the electron delocalization processes occur in the studied systems.
A novel (2E)-1-(5-chlorothiophen-2-yl)-3-{4-[(E)-2-phenylethenyl]phenyl}prop-2-en-1-one [C21H15ClOS] compound has been synthesized and its structure has been characterized by FT-IR, Raman and single-crystal X-ray diffraction techniques. The conformational isomers, optimized geometric parameters, normal mode frequencies and corresponding vibrational assignments of the compound have been examined by means of HF, MP2, BP86, BLYP, BMK, B3LYP, B3PW91, B3P86 and M06-2X functionals. Reliable vibrational assignments and molecular orbitals have been investigated by the potential energy distribution and natural bonding orbital analyses, respectively. The compound crystallizes in the triclinic space group P-1 with the cis-trans-trans form. There is a good agreement between the experimentally determined structural parameters and vibrational frequencies of the compound and those predicted theoretically using the density functional theory with the BLYP and BP86 functionals.
Ferroelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) copolymer 70/30 thin films are prepared by spin coating. The crystalline structure of these films is investigated by varying the annealing temperature from the ferroelectric phase to the paraelectric phase. A hot plate was used to produce a direct and an efficient annealing effect on the thin film. The dielectric, ferroelectric and pyroelectric properties of the P(VDF-TrFE) thin films are measured as a function of different annealing temperatures (80 to 140 °C). It was found that an annealing temperature of 100 °C (slightly above the Curie temperature, Tc) has induced a highly crystalline β phase with a rod-like crystal structure, as examined by X-ray. Such a crystal structure yields a high remanent polarization, Pr = 94 mC/m2, and pyroelectric constant, p = 24 μC/m2K. A higher annealing temperature exhibits an elongated needle-like crystal domain, resulting in a decrease in the crystalline structure and the functional electrical properties. This study revealed that highly crystalline P(VDF-TrFE) thin films could be induced at 100 °C by annealing the thin film with a simple and cheap method.
Microwave breast imaging has been reported as having the most potential to become an alternative or additional tool to the existing X-ray mammography technique for detecting breast tumors. Microwave antenna sensor performance plays a significant role in microwave imaging system applications because the image quality is mostly affected by the microwave antenna sensor array properties like the number of antenna sensors in the array and the size of the antenna sensors. In this paper, a new system for successful early detection of a breast tumor using a balanced slotted antipodal Vivaldi Antenna (BSAVA) sensor is presented. The designed antenna sensor has an overall dimension of 0.401λ × 0.401λ × 0.016λ at the first resonant frequency and operates between 3.01 to 11 GHz under 10 dB. The radiating fins are modified by etching three slots on both fins which increases the operating bandwidth, directionality of radiation pattern, gain and efficiency. The antenna sensor performance of both the frequency domain and time domain scenarios and high-fidelity factor with NFD is also investigated. The antenna sensor can send and receive short electromagnetic pulses in the near field with low loss, little distortion and highly directionality. A realistic homogenous breast phantom is fabricated, and a breast phantom measurement system is developed where a two antennas sensor is placed on the breast model rotated by a mechanical scanner. The tumor response was investigated by analyzing the backscattering signals and successful image construction proves that the proposed microwave antenna sensor can be a suitable candidate for a high-resolution microwave breast imaging system.
High demand of semiconductor gas sensor works at low operating temperature to as low as 100 °C has led to the fabrication of gas sensor based on TiO₂ nanoparticles. A sensing film of gas sensor was prepared by mixing the sensing material, TiO₂ (P25) and glass powder, and B₂O₃ with organic binder. The sensing film was annealed at temperature of 500 °C in 30 min. The morphological and structural properties of the sensing film were characterized by field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD). The gas sensor was exposed to hydrogen with concentration of 100⁻1000 ppm and was tested at different operating temperatures which are 100 °C, 200 °C, and 300 °C to find the optimum operating temperature for producing the highest sensitivity. The gas sensor exhibited p-type conductivity based on decreased current when exposed to hydrogen. The gas sensor showed capability in sensing low concentration of hydrogen to as low as 100 ppm at 100 °C.
Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.
Formation of carbonate minerals by CO2 sequestration is a potential means to reduce atmospheric CO2 emissions. Vast amount of alkaline and alkali earth metals exist in silicate minerals that may be carbonated. Laboratory experiments carried out to study the dissolution rate in Pahang Sandstone, Malaysia, by CO2 injection at different flow rate in surficial condition. X-ray Powder Diffraction (XRD), Scanning Electron Microscope (SEM) with Energy Dispersive X-ray Spectroscopy (EDX), Atomic Absorption Spectroscopy (AAS) and weight losses measurement were performed to analyze the solid and liquid phase before and after the reaction process. The weight changes and mineral dissolution caused by CO2 injection for two hours CO2 bubbling and one week' aging were 0.28% and 18.74%, respectively. The average variation of concentrations of alkaline earth metals in solution varied from 22.62% for Ca(2+) to 17.42% for Mg(2+), with in between 16.18% observed for the alkali earth metal, potassium. Analysis of variance (ANOVA) test is performed to determine significant differences of the element concentration, including Ca, Mg, and K, before and after the reaction experiment. Such changes show that the deposition of alkali and alkaline earth metals and the dissolution of required elements in sandstone samples are enhanced by CO2 injection.
This study investigated the dry and aqueous erosion of mild steel using electrochemical and dry sand impact techniques. In dry sand impact experiments, mild steel was eroded with 45 μm and 150 μm sand particles. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX) and micro-hardness techniques were used to elaborate the surface morphology of the eroded samples. The results revealed significant change in morphology of the eroded samples. In-depth analysis showed that although the metal erosion due to larger particles was significantly higher, the fines also notably damaged the metal surface. The surface damages were appreciably reduced with decrease in impact angle of the accelerated particles. The maximum damages were observed at an impact angle of 90°. The hardness of the samples treated with 45 μm and 150 μm sand remained in the range of 88.34 to 102.31 VHN and 87.7 to 97.55 VHN, respectively. In electrochemical experiments, a triple electrode probe was added into the metal treatment process. The linear polarization resistance (LPR) measurements were performed in slurries having 5% (by weight) of sand particles. LPR of the samples treated with 45 μm and 150 μm sand slurries was calculated about 949 Ω.cm(2) and 809 Ω.cm(2), respectively.
In this work, copper pyrovanadate (Cu3V2O7(OH)2(H2O)2) nanoparticles have been synthesized by a simple and rapid chemical precipitation method. Different copper-organic complexes were used to control the size and morphology of products. The morphology and structure of the as-synthesized products were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FT-IR) spectrum, electron dispersive X-ray spectroscopy (EDX), thermal gravimetric analysis (TGA), differential thermal analysis (DTA) and photoluminescence (PL) spectroscopy. The influence of copper pyrovanadate nanostructures on the flame retardancy of the polystyrene, poly vinyl alcohol and cellulose acetate was studied. Dispersed nanoparticles play the role of a magnetic barrier layer, which slows down product volatilization and prevents the flame and oxygen from the sample during decomposition of the polymer. Cu3V2O7(OH)2(H2O)2 is converted to Cu3V2O8 with an endothermic reaction which simultaneously releases water and decrease the temperature of the flame region.
In this work, zinc chromite (ZnCr2O4) nanostructures have been synthesized through co-precipitation method. The effect of various parameters such as alkaline agent, pH value, and capping agent type was investigated on purity, particle size and morphology of samples. It was found that particle size and morphology of the products could be greatly influenced via these parameters. The synthesized products were characterized by field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), fourier transform infrared (FT-IR) spectra, X-ray energy dispersive spectroscopy (EDS), photoluminescence (PL) spectroscopy, diffuse reflectance spectroscopy (DRS) and vibrating sample magnetometry (VSM). The superhydrophilicity of the calcined oxides was investigated by wetting experiments and a sessile drop technique which carried out at room temperature in air to determine the surface and interfacial interactions. Furthermore, the photocatalytic activity of ZnCr2O4 nanoparticles was confirmed by degradation of anionic dyes such as Eosin-Y and phenol red under UV light irradiation. The obtained ZnCr2O4 nanoparticles exhibit a paramagnetic behavior although bulk ZnCr2O4 is antiferromagnetic, this change in magnetic property can be ascribed to finite size effects.
In this paper, the electrical, dielectric, Raman and small angle X-ray scattering (SAXS) structure behavior of disposed transformer oil in the presence of multi-walled carbon nanotube (MWCNT) were systematically tested to verify their versatility for preparing better alternative transformer oil in future. MWCNT nanofluids are prepared using a two-step method with concentrations ranging from 0.00 to 0.02 g/L. The test results reveal that 0.005 g/L concentration possesses the most optimum performance based on the electrical (AC breakdown and lightning impulse) and dielectric (permittivity, dissipation factor and resistivity) behavior. According to the trend of AC breakdown strength and lightning impulse pattern, there were 212.58% and 40.01% enhancement indicated for 0.005 g/L concentration compared to the disposed transformer oil. The presence of MWCNT also yielding to the decrement of dissipation factor, increased on permittivity and resistivity behavior of disposed transformer oil which reflected to the performance of electrical properties. Furthermore, it is found that these features correlated to the structural properties as systematically verify by Raman and SAXS analysis study.
Observations and modeling studies have shown that during CO2 injection into underground carbonate reservoirs, the dissolution of CO2 into formation water forms acidic brine, leading to fluid-rock interactions that can significantly impact the hydraulic properties of the host formation. However, the impacts of these interactions on the pore structure and macroscopic flow properties of host rock are poorly characterized both for the near-wellbore region and deeper into the reservoir. Little attention has been given to the influence of pressure drop from the near-wellbore region to reservoir body on disturbing the ionic equilibrium in the CO2-saturated brine and consequent mineral precipitation. In this paper, we present the results of a novel experimental procedure designed to address these issues in carbonate reservoirs. We injected CO2-saturated brine into a composite core made of two matching grainstone carbonate core plugs with a tight disk placed between them to create a pressure profile of around 250 psi resembling that prevailing in reservoirs during CO2 injection. We investigated the impacts of fluid-rock interactions at pore and continuum scale using medical X-ray CT, nuclear magnetic resonance, and scanning electron microscopy. We found that strong calcite dissolution occurs near to the injection point, which leads to an increase in primary intergranular porosity and permeability of the near injection region, and ultimately to wormhole formation. The strong heterogeneous dissolution of calcite grains leads to the formation of intra-granular micro-pores. At later stages of the dissolution, the internal regions of ooids become accessible to the carbonated brine, leading to the formation of moldic porosity. At distances far from the injection point, we observed minimal or no change in pore structure, pore roughness, pore populations, and rock hydraulic properties. The pressure drop of 250 psi slightly disturbed the chemical equilibrium of the system, which led to minor precipitation of sub-micron sized calcite crystals but due to the large pore throats of the rock, these deposits had no measurable impact on rock permeability. The trial illustrates that the new procedure is valuable for investigating fluid-rock interactions by reproducing the geochemical consequences of relatively steep pore pressure gradients during CO2 injection.
Various techniques are commonly used to produce nano-crystalline NiAl2O4 materials; however, their practical applications in the microwave region remain very limited. In this work, flexible substrates for metamaterials containing two different concentrations of NiAl2O4 (labelled Ni36 and Ni42) have been synthesised using a sol-gel method. The formation of spinel structures in the synthesised materials is confirmed, and their crystalline sizes are determined using scanning electron microscopy, X-ray diffraction, and energy dispersive X-ray techniques. The dielectric properties, conductivities, loss tangents, and other parameters of the NiAl2O4-based substrates are analysed to evaluate their applicability as dielectric materials for the microwave frequency range. The obtained results show that the fabricated Ni36 and Ni42 nickel aluminates possess dielectric constants of 4.94 and 4.97 and loss tangents of 0.01 and 0.007, respectively; in addition, they exhibit high flexibility and light weight, which make them suitable for applications as metamaterial substrates. The synthesised structures are also validated experimentally using a commercially available electromagnetic simulator; as a result, double negative behaviour of the flexible metamaterials has been observed. Furthermore, it is found that the prepared NiAl2O4 substrates can be used in the S-, C-, and X-bands of the microwave frequency region.
The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.
A polystyrene (PS)-anchored Pd(II) metal complex was synthesized on cross-linked polymer by heating a mixture of chlorometylated polystyrene with phenyldithiocarbazate and carbon disulfide in the presence of potassium hydroxide (KOH) in dimethylformamide (DMF). The reaction mixture was heated at 80 °C to form the corresponding phenyldithiocarbazate-functionalized polymer. Then, it was treated with bis(benzonitrile)palladium(II) chloride. The properties of dark colored polymer, impregnated with the metal complex was then characterized by various spectroscopic technique such as Fourier Transform Infrared (FTIR), Scanning Electron Microscopy/Energy Dispersive X-ray (SEM/EDX), CHNS elemental analysis, BET surface area, X-ray Diffraction (XRD), Thermogravimetric (TGA) and Inductively Coupled Plasma-Optical Emission (ICP-OES) spectroscopy.
The synthesis of graphene by double thermal chemical vapor deposition (DTCVD) using waste of industrial cooking oil (WICO) as a natural carbon source was investigated. The synthesis parameter (Argon gas flow rate) was varied between 50sccm to 300sccm by 50sccm increments. The function of Argon gas is to provide ambient condition, remove the atmospheric air from the tube and could improve the crystallinity of graphene during synthesis. WICO (from AYAMAS food processing) was placed in the first furnace (precursor furnace) and nickel was placed in the second furnace (deposition furnace). During the synthesis, elevated quantities of carbon from the source material are separated and precipitated on the Nickel surface. The sample were characterized by using Field Emission Scanning Electron Microscopy (FESEM), X-Ray Diffraction (XRD), Energy Dispersive X-ray (EDX), and Ultraviolet Visible (UV-Vis) spectroscopy. Based on FESEM images, at 250sccm, hexagonal graphene formation was observed. Besides, optical properties can be seen by UV-Vis and as the results show that 250sccm is the highest reflectivity value. Consequently, graphene synthesis from WICO using various Argon gas flow rate as precursor is successfully demonstrated.
The aim of this study was to investigate the potential of treated rice husk ash (RHA) as adsorbent to adsorb acidic SO2 gas. The treated RHA was prepared using a water hydration method by mixing the RHA, Calcium oxide (CaO) and Sodium Hydroxide (NaOH). The addition of NaOH is to increase the dissolution of silica from the RHA to form reactive species responsible for higher desulfurization activity. The untreated and treated RHA were subjected to several characterizations and the characteristics of the adsorbents were compared. The functional groups present on the surface of the adsorbent were determined using Fourier Transform Infrared (FTIR). The chemical composition of the untreated and treated RHA was analyzed by using X-ray Fluorescence (XRF). Scanning electron microscope (SEM) analysis showed that the treated RHA has higher porosity compared to untreated RHA. Based on the SO2 adsorption analysis, it was found that the treated RHA has higher adsorption capacity, 62.22 mg/g, compared to untreated RHA, 1.49 mg/g.
The microencapsulation of three model drugs; metronidazole, paracetamol and sulphapyridine into Poly (dl-Lactide-Co-Glycolide) (PLGA) scaffolds were probed using X-ray Powder Diffraction (XRPD). Changes in the diffraction patterns of the PLGA scaffolds after encapsulation was suggestive of a chemical interaction between the pure drugs and the scaffolds and not a physical intermixture.